Intelligent anti-theft and tracking method and system for cargo transportation

Abstract

The invention discloses an intelligent anti-theft and tracking method and system for cargo transportation, and the method comprises the steps: dynamically adjusting the collection frequency of a sensor through introducing a risk weight coefficient, combining the real-time position information of a cargo and an environment state vector, thereby accurately evaluating the risk level of an area where the cargo is located, and carrying out the intelligent anti-theft and tracking of the cargo on the basis. According to the method, a multi-level dynamic region chain is constructed, priority processing and redundant storage are carried out on key data in a high-risk region, and meanwhile, the consistency and integrity of data in the cross-region transportation process are ensured through a global chain synchronization mechanism; besides, the built cross-region cargo anomaly prediction model realizes real-time monitoring and anomaly identification of the cargo state through a multi-region data fusion rule; the method has the remarkable advantages in the aspects of improving the cargo transportation safety, achieving accurate tracking and enhancing the reliability and safety of data processing, and an innovative and practical solution is provided for logistics management at home and abroad.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to an intelligent anti-theft and tracking method and system for cargo transportation. Background Art

[0002] With the acceleration of globalization, the rapid development of international trade and logistics industries, the safety and efficiency of cargo transportation have attracted more and more attention. In the traditional cargo transportation process, the method of relying on human monitoring and mechanical locking can no longer meet the efficiency and safety requirements of modern logistics. With the application of emerging technologies such as the Internet of Things (IoT), big data analysis, and blockchain, cargo transportation management is moving towards intelligence and automation. Especially in the complex environment of multinational supply chains, how to effectively monitor the status of goods, prevent the loss and damage of goods during transportation, and ensure the timely tracking of goods has become a research hotspot in the field of logistics. At present, the cargo tracking system based on GPS positioning technology has been widely used and can obtain the location information of goods in real time. However, GPS positioning technology often only focuses on the static position of goods, ignores the impact of environmental factors on the safety of goods, and does not respond to abnormal situations in a timely manner. In addition, traditional anti-theft systems mainly rely on physical locks and alarm devices, which are difficult to cope with the increasingly complex security threats in the modern logistics environment.

[0003] Nowadays, although we have made some progress in the technical research of cargo location tracking, there are still some problems in the real-time anti-theft and tracking, as well as the security and reliability of data processing. First of all, most traditional cargo tracking systems lack the ability to comprehensively analyze environmental data and cannot accurately assess the risk area where the cargo is located. For example, during transportation, the safety of cargo may be affected by many factors due to changes in climate conditions and potential theft risks. This not only increases the possibility of cargo being lost or damaged in high-risk areas, but also makes the response to abnormal situations relatively delayed. In addition, traditional logistics data management systems usually adopt a centralized database structure, which is vulnerable to single point failures and data tampering, and cannot meet the data security and consistency requirements of cross-regional, multi-node logistics networks. Although blockchain technology has obvious advantages in data security and transparency, the existing blockchain applications in logistics management have not fully considered the complexity of dynamic data processing and cross-regional data fusion. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides an intelligent anti-theft and tracking method for cargo transportation to solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an intelligent anti-theft and tracking method for cargo transportation, comprising:

[0008] Obtain the current location, environmental conditions, and transportation status of each batch of goods, adjust the acquisition frequency of sensor signals in the cargo box, and determine the risk area where the current batch of goods is located based on the acquisition frequency of sensor signals;

[0009] Construct a multi-level dynamic regional chain, pre-process the sensor signal, transmit the pre-processed signal to the dynamic regional chain, and define the rules for uploading cargo data;

[0010] Construct a cross-regional cargo anomaly prediction model, define multi-regional data fusion rules, identify whether the current batch of cargo data has any anomalies when passing through a certain area, and if so, use the current output result of the cross-regional cargo anomaly prediction model to update the multi-level dynamic regional chain, otherwise, redefine the cargo data chain rules.

[0011] As a preferred solution of the intelligent anti-theft and tracking method for cargo transportation described in the present invention, the current location, environmental conditions and transportation status of each batch of cargo are obtained, including:

[0012] The latitude and longitude data of the current truck travel are obtained through GPS to obtain the current cargo position signal, and the position signal is smoothed to eliminate the noise around the cargo;

[0013] According to the sensors deployed in the cargo box, the sensors are normalized to collect environmental data around the cargo in real time;

[0014] The processed cargo position signal is used to calculate the moving speed and direction of the cargo, and the environmental state vector at the current moment is defined by combining the environmental data with the moving speed and direction of the cargo.

[0015] As a preferred solution of the intelligent anti-theft and tracking method for cargo transportation described in the present invention, wherein: adjusting the acquisition frequency of the sensor signal in the cargo box, and determining the risk area where the current batch of cargo is located according to the acquisition frequency of the sensor signal, includes:

[0016] Introducing a risk weight coefficient, and adjusting the acquisition frequency of the sensor signal in the cargo box according to the environmental state vector;

[0017] The risk index of the goods is obtained through the adjusted collection frequency and the processed goods location signal, and the risk area where the current batch of goods is located is divided according to the maximum and minimum values ​​of the risk index.

[0018] As a preferred solution of the intelligent anti-theft and tracking method for cargo transportation described in the present invention, a multi-level dynamic regional chain is constructed, including:

[0019] Build an independent regional chain in each cargo transportation area to store the sensor data, cargo location data and environmental data of the cargo in the area;

[0020] The data information stored in each regional chain is collected and synchronized through the global chain, and the data stored in different regional chains is verified using the consensus algorithm. If the goods cross the regional boundary, that is, from the current regional chain to another regional chain, the other regional chain receives the data on the current regional chain and uses the global chain to exchange data with the current regional chain;

[0021] If the location of the goods in the regional chain is found to be in a high-risk area during storage, the regional chain will be switched to the dynamic chain, and the dynamic chain will process the data. When the data processing is completed, the dynamic chain will switch back to the regional chain and synchronize the data to the regional chain and the global chain.

[0022] As a preferred solution of the intelligent anti-theft and tracking method for cargo transportation described in the present invention, it also includes:

[0023] The set of sensor data within a certain time window will be packaged into a data block, and the packaged data block will generate a unique identifier through a hash function;

[0024] For key cargo data whose location is in a high-risk area, it is divided into multiple data slices and stored on multiple regional chain nodes, where each data slice is redundantly stored using erasure codes.

[0025] As a preferred solution of the intelligent anti-theft and tracking method for cargo transportation described in the present invention, the sensor signal is preprocessed, the preprocessed signal is transmitted to the dynamic area chain, and the rules for uploading cargo data are defined, including:

[0026] Before the cargo data is uploaded to the chain, the standard deviation and mean of the sensor data are calculated, and data points that deviate from the mean by more than the mean of the data block are eliminated;

[0027] For cargo data in low-risk areas and medium-risk areas, the changes in the cargo data are compressed and stored on the chain.

[0028] As a preferred solution of the intelligent anti-theft and tracking method for cargo transportation described in the present invention, a cross-region cargo anomaly prediction model is constructed, multi-region data fusion rules are defined, and whether the current batch of cargo data is abnormal when passing through a certain area is identified, including:

[0029] The cargo location data, environmental data and transportation status data are represented by dimension vectors, and the dimension vectors of the cargo location data, environmental data and transportation status data are aggregated by a sliding window;

[0030] Analyze the sensor data of the goods passing through the current area to obtain the key data types of the area, and assign weights to the areas where the goods have passed in the past and in the future. Fuse the data information stored on the chain of each area with the output results of the cross-regional goods anomaly prediction model, and output the anomaly probability of the goods passing through the area according to the weighted average method;

[0031] When the abnormal probability exceeds 60%, it indicates that an abnormality exists in the area where the goods pass through.

[0032] In a second aspect, the present invention provides an intelligent anti-theft and tracking system for cargo transportation, comprising:

[0033] The cargo data and sensor data acquisition module is used to obtain the current location, environmental conditions and transportation status of each batch of cargo, adjust the acquisition frequency of sensor signals in the cargo box, and determine the risk area where the current batch of cargo is located based on the acquisition frequency of sensor signals;

[0034] The sensor signal processing module is used to build a multi-level dynamic regional chain, pre-process the sensor signal, transmit the pre-processed signal to the dynamic regional chain, and define the rules for uploading cargo data;

[0035] The cargo data anomaly processing module is used to build a cross-regional cargo anomaly prediction model, define multi-regional data fusion rules, and identify whether the current batch of cargo data has anomalies when passing through a certain area. If there are anomalies, the current output results of the cross-regional cargo anomaly prediction model are used to update the multi-level dynamic regional chain. Otherwise, the cargo data chain rules are redefined.

[0036] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: the processor implements any step of the above method when executing the computer program.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program implements any step of the above method when executed by a processor.

[0038] Compared with the prior art, the invention has the following beneficial effects:

[0039] 1. The present invention introduces a risk weight coefficient, combines the environmental state vector, adjusts the sensor acquisition frequency in real time, and prioritizes key data in high-risk areas, thereby forming an intelligent anti-theft mechanism. This intelligent anti-theft mechanism effectively addresses the potential safety hazards caused by environmental changes in the prior art, and significantly improves the safety of goods in complex transportation environments;

[0040] 2. Based on the combination of acquired GPS data and multi-regional data fusion technology, accurate tracking of goods throughout cross-regional transportation is achieved. In particular, through the synchronization mechanism of the global chain and the regional chain, it ensures that no matter where the goods are located, their location and status can be monitored in real time and accurately, thus solving the problem of incoherent cross-regional tracking in existing technologies;

[0041] 3. Build a multi-level dynamic regional chain, especially in high-risk areas, use dynamic chains for data processing and redundant storage, effectively prevent data loss or tampering, and verify regional chain data through the global chain, further improve the security and reliability of logistics data management, and overcome security vulnerabilities in existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0043] Figure 1 This is an overall flow chart of an intelligent anti-theft and tracking method for cargo transportation according to an embodiment of the present invention;

[0044] Figure 2 A graph showing changes in data collection accuracy during batch cargo transportation in an intelligent anti-theft and tracking method for cargo transportation according to an embodiment of the present invention;

[0045] Figure 3 This is a diagram showing the influence of sensor acquisition frequency on positioning accuracy in an intelligent anti-theft and tracking method for cargo transportation according to an embodiment of the present invention;

[0046] Figure 4 This is a result diagram of the response time and reaction speed of each sensor to an emergency event in the intelligent anti-theft and tracking method for cargo transportation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0050] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0051] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0052] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0053] Example 1

[0054] Reference Figure 1, which is the first embodiment of the present invention, provides an intelligent anti-theft and tracking method for cargo transportation, comprising:

[0055] S1. Obtain the current location, environmental conditions and transportation status of each batch of goods, adjust the acquisition frequency of sensor signals in the cargo box, and determine the risk area where the current batch of goods is located based on the acquisition frequency of sensor signals;

[0056] Furthermore, the latitude and longitude data of the current truck travel is obtained through GPS to obtain the current cargo position signal, and the position signal is smoothed to eliminate the noise around the cargo;

[0057] Specifically, cargo data includes the current location, environmental conditions, and transportation status of the cargo;

[0058] Specifically, the signal is smoothed, and the formula is:

[0059]

[0060] in, is the state estimation at the current moment, A is the state transfer matrix, B is the control input matrix, K t is the Kalman gain, z t is the measurement value, H is the measurement matrix;

[0061] It should be noted that The size determines the collection frequency of each sensor. The higher the collection frequency, the greater the regional risk level.

[0062] Furthermore, according to the sensors deployed in the cargo box, the sensors are normalized to collect environmental data around the cargo in real time;

[0063] It should be noted that by obtaining the latitude and longitude data of the truck and combining it with the sensors deployed in the cargo box, the current location of the goods can be clearly known, thereby preventing the goods from being replaced midway;

[0064] Specifically, temperature sensors, humidity sensors, light sensors, and vibration sensors are used to collect environmental data around the goods in real time;

[0065] Specifically, each sensor is normalized:

[0066]

[0067] Among them, T(t) is temperature data, H(t) is humidity data, a(t) is vibration acceleration, and L(t) is light intensity;

[0068] It should be noted that the normalization process unifies the dimensions of different sensor data, ensures the consistency of various environmental data during analysis, and avoids calculation errors caused by different dimensions;

[0069] Furthermore, the processed cargo position signal is used to calculate the cargo moving speed and direction, and the environmental state vector at the current moment is defined by combining the environmental data and the cargo moving speed and direction;

[0070] It should be noted that the speed and direction of movement of the goods represent the transportation status of the goods;

[0071] Specifically, the moving speed and direction of the goods are:

[0072]

[0073] Among them, Δlat and Δlon are the changes in longitude and latitude at adjacent time points, Δt is the time interval, v(t) is the moving speed of the goods, and a v (t) is the direction of the cargo;

[0074] Specifically, the current environment state vector is defined as:

[0075] S(t)=[T′(t),H′(t),a′(t),L′(t),v(t),a v (t)]

[0076] It should be noted that because the moving speed and direction of the goods reflect the dynamic behavior of the goods in transportation, combined with the environmental state vector, it can more accurately determine whether the goods are in a normal transportation state, such as the sudden acceleration or deceleration of the goods during transportation, and the goods staying in a certain area for too long;

[0077] S2. Build a multi-level dynamic regional chain, pre-process the sensor signals, transmit the pre-processed signals to the dynamic regional chain, and define the rules for uploading cargo data;

[0078] Furthermore, a risk weight coefficient is introduced to adjust the acquisition frequency of sensor signals in the cargo box according to the environmental state vector;

[0079] Specifically, the calculation formula of the risk weight coefficient is:

[0080] ω R (t)=|T′(t)―T′ ref |+|H′(t)―H′ ref |+|a′(t)―a′ ref |+v(t)+a v (t)

[0081] Among them, T′ref , H′ ref , a′ ref It is the average value of temperature sensor, humidity sensor and vibration sensor under normal environment;

[0082] Specifically, the acquisition frequency of the sensor signal in the cargo box is adjusted to:

[0083] f(t)=S(t)+k×ω R (t)

[0084] Where k is the frequency modulation coefficient;

[0085] Furthermore, the risk index of the goods is obtained through the adjusted collection frequency and the processed goods location signal, and the risk area where the current batch of goods is located is divided according to the maximum and minimum values ​​of the risk index;

[0086] Specifically, the cargo risk index is:

[0087] R(t)=GIS_Risk(lat,lon)+ω R (t)

[0088] Among them, GIS_Risk is the GIS risk coefficient corresponding to the current longitude and latitude;

[0089] Specifically, the risk area division rules are as follows:

[0090] Low risk area: R(t) <R low

[0091] Medium risk areas:

[0092] High-risk areas:

[0093] It should be noted that when the goods enter the high-risk area, the sensor acquisition frequency reaches the maximum value, ensuring the sensitive response of the multi-level dynamic regional chain to environmental changes;

[0094] Furthermore, an independent regional chain is constructed in each cargo transportation area to store sensor data, cargo location data and environmental data of the cargo in the area;

[0095] It should be explained that, since the transportation status of the goods has been considered before, it does not need to be considered in the multi-level dynamic regional chain;

[0096] It should be noted that independent regional chains ensure that data on any regional chain can be effectively protected and verified during data transmission and storage. It combines the distributed storage characteristics of blockchain technology, which is different from the traditional centralized database management method, and reduces the risk of single point failure.

[0097] Furthermore, the data information stored in each regional chain is collected and synchronized through the global chain, and the data stored in different regional chains is verified using the consensus algorithm. If the goods cross the regional boundary, that is, from the current regional chain to another regional chain, the other regional chain receives the data on the current regional chain and uses the global chain to exchange data with the current regional chain;

[0098] Furthermore, if the location of the goods in the regional chain is found to be in a high-risk area during storage, the regional chain will be switched to the dynamic chain, and the dynamic chain will process the data. When the data processing is completed, the dynamic chain will switch back to the regional chain and synchronize the data to the regional chain and the global chain;

[0099] It should be noted that the establishment of a global chain can not only achieve the traceability of goods abroad, but is also applicable domestically, and it only requires replacing the global chain with a relay chain;

[0100] Specifically, if the cargo location is in a medium-risk or low-risk area, the data will be recorded in the regional chain by default and synchronized across regions through the global chain;

[0101] It should be noted that the concept of dynamic chain is introduced into the multi-level dynamic regional chain to cope with the data processing needs of high-risk areas, which enhances the flexibility and response ability of the blockchain architecture when encountering risks;

[0102] Furthermore, the set of sensor data within a certain time window will be packaged into a data block, and the packaged data block will generate a unique identifier through a hash function;

[0103] Specifically, the packaging process is as follows:

[0104] B(t)={D(t 1 ),D(t 2 ),…,D(t n )}t 1 ,t 2 ,…,t n ∈T w

[0105] H(B(t))=H({D(t 1 ),D(t 2 ),…,D(t n )})

[0106] Where B(t) is the packed data block, H(·) is the hash function, H(B(t)) is the identifier, and D(t)

[0107] is the aggregated sensor data;

[0108] Furthermore, for critical cargo data whose location is in a high-risk area, it is divided into multiple data slices and stored on multiple regional chain nodes, where each data slice is redundantly stored through erasure coding;

[0109] Specifically, the sharding formula is:

[0110] S i =Slice(B(t),i) i∈[1,m]

[0111] Among them, S i is the data slice, S i ={S 1 ,S 2 ,S 3 ,…,S m};

[0112] It should be explained that Erasure Code (EC) is a data protection method that divides data into fragments and generates redundant data blocks through encoding. These data blocks are stored in different locations, such as disks, storage nodes or other geographical locations to improve data reliability and fault tolerance.

[0113] It should be noted that the use of erasure codes ensures that even if some nodes fail, data can still be recovered through other nodes. On the other hand, it can improve the availability and tamper-proofness of data.

[0114] Furthermore, before the cargo data is uploaded to the chain, the standard deviation and mean of the sensor data are calculated, and data points that deviate from the mean by more than the mean of the data block are eliminated;

[0115] Specifically, the filtering (elimination) formula is expressed as:

[0116] D filtered (t) = {D(t) | B σD (t)―B μD (t)≤D(t)≤B σD (t)+B μD (t)}

[0117] Among them, B σD (t) is the mean value of sensor data, B μD (t) is the standard deviation of the sensor data;

[0118] Furthermore, for cargo data in low-risk and medium-risk areas, the changes in cargo data are compressed and stored on the chain;

[0119] Specifically, the process of compressing the change in cargo data is as follows:

[0120] ΔD(t)=D(t)―D(t―1)

[0121] Among them, ΔD(t) is the change in cargo data after compression, and D(t-1) is the sensor data at the previous moment;

[0122] It should be noted that after the cargo data is compressed, it is passed through a hash function to generate a data check code, and the data is checked during transmission and on-chain to ensure data consistency during transmission;

[0123] S3. Construct a cross-regional cargo anomaly prediction model, define multi-regional data fusion rules, identify whether the current batch of cargo data has anomalies when passing through a certain area, and if so, use the current output results of the cross-regional cargo anomaly prediction model to update the multi-level dynamic regional chain. Otherwise, redefine the cargo data chain rules;

[0124] Further, the cargo location data, environmental data and transportation status data are represented by dimension vectors, and the dimension vectors of the cargo location data, environmental data and transportation status data are aggregated by using a sliding window;

[0125] Specifically, the aggregation method is:

[0126] X t =[T′(t―n),H′(t―n),a′(t―n),…,T′(t),H′(t),a′(t)]

[0127] Where n is the length of the sliding window;

[0128] Furthermore, the sensor data of the goods passing through the current area is analyzed to obtain the key data types of the area, and weights are assigned to the areas where the goods have passed in the past and in the future. The data information stored on the chain of each area and the output results of the cross-regional goods abnormality prediction model are integrated, and the abnormal probability of the goods passing through the area is output according to the weighted average method;

[0129] Specifically, the weight distribution is:

[0130]

[0131] Among them, Y(t) is the actual output value of the model, is the predicted output value; is the future weight of the goods, W i The past weight of the goods;

[0132] Specifically, the fusion formula is:

[0133]

[0134] It should be noted that the sensor data in each region may have different characteristics. For example, in cold areas, temperature changes are more critical, while in busy ports, location information and vibration data are more important. Therefore, it is necessary to analyze the characteristics of sensor data in each region and define the key data types in the region.

[0135] Preferably, when the abnormal probability exceeds 60%, it is indicated that there is an abnormality in the area where the goods pass through;

[0136] Specifically, the rules for uploading cargo data to the blockchain are redefined to modify the value range of sensor data in the filtering (elimination) formula;

[0137] It should be noted that by defining a cross-regional cargo anomaly prediction model, it is possible to effectively identify whether there are any anomalies when the cargo passes through the region during transportation, thereby achieving real-time monitoring of each batch of cargo.

[0138] Furthermore, this embodiment also provides an intelligent anti-theft and tracking system for cargo transportation, comprising:

[0139] The cargo data and sensor data acquisition module is used to obtain the current location, environmental conditions and transportation status of each batch of cargo, adjust the acquisition frequency of sensor signals in the cargo box, and determine the risk area where the current batch of cargo is located based on the acquisition frequency of sensor signals;

[0140] The sensor signal processing module is used to build a multi-level dynamic regional chain, pre-process the sensor signal, transmit the pre-processed signal to the dynamic regional chain, and define the rules for uploading cargo data;

[0141] The cargo data anomaly processing module is used to build a cross-regional cargo anomaly prediction model, define multi-regional data fusion rules, and identify whether the current batch of cargo data has anomalies when passing through a certain area. If there are anomalies, the current output results of the cross-regional cargo anomaly prediction model are used to update the multi-level dynamic regional chain. Otherwise, the cargo data chain rules are redefined.

[0142] This embodiment also provides a computer device, which is applicable to the case of the intelligent anti-theft and tracking method for cargo transportation, including:

[0143] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the intelligent anti-theft and tracking method for cargo transportation proposed in the above embodiment.

[0144] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0145] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent anti-theft and tracking method for cargo transportation proposed in the above embodiment is implemented.

[0146] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0147] Example 2

[0148] Reference Figures 2 to 4 , which is the second embodiment of the present invention, and which provides an intelligent anti-theft and tracking method for cargo transportation, comprising: through simulation experiments, aiming to verify the effectiveness of the proposed intelligent anti-theft and tracking method for cargo transportation, especially the application effect in a complex transportation environment;

[0149] In the simulation experiment, 100 batches of different types of goods were selected, including high-value electronic products, fragile goods and daily necessities; these goods were loaded in different trucks and assigned to different transportation routes; these routes passed through a variety of typical transportation environments such as cities, rural areas, highways and ports; in order to ensure the comprehensiveness of the simulation experiment, a variety of sensors were installed on the trucks, including GPS modules, temperature sensors, humidity sensors, vibration sensors and light sensors; in addition, a data acquisition system was used to monitor and record sensor data; the current location, environmental conditions and transportation status of each batch of goods will be monitored in real time; sensor data is transmitted and stored through a multi-level dynamic regional chain, and a cross-regional cargo anomaly prediction model is used to evaluate the safety of each batch of goods. Risk status; During the transportation of each batch of goods, the latitude and longitude data of the truck is obtained in real time through GPS, and smoothed to eliminate noise. The sensor collects environmental data such as temperature, humidity, vibration and light around the goods in real time, and normalizes it. Combined with the moving speed and direction of the goods, the environmental state vector is calculated to more accurately evaluate the transportation status of the goods; During the simulation experiment, the acquisition frequency of the sensor signal is dynamically adjusted according to the environmental state vector, and the risk area where the goods are located is divided by calculating the risk index. When the goods enter the high-risk area, the acquisition frequency of the sensor reaches the maximum value to ensure high-frequency data collection and analysis. At the same time, the independent regional chain ensures the security and consistency of data storage and transmission in different areas;

[0150] In order to verify the effectiveness of the intelligent anti-theft and tracking system, multiple simulated emergencies were set in the simulation experiment, including sudden temperature changes, increased vibration, and cargo being stranded at a certain location for a long time. These events were identified and responded to through the abnormal prediction model, and the data chain rules were adjusted to ensure the safety of the cargo and the accuracy of the data. Four batches were randomly selected for display, as shown in Table 1.

[0151] Table 1

[0152]

[0153]

[0154] From Table 1, we can see that (Batch 4) has a sudden increase in vibration acceleration to 0.8m / s 2After that, the collection frequency was immediately increased from 20Hz to 30Hz, and the risk index was adjusted to 1.0, indicating that the goods were in an extremely high-risk state. In this case, the traditional method may not be able to detect such violent vibrations in time, resulting in damage to the goods. The method of the present invention can not only detect quickly, but also further confirm the state of the goods by increasing the collection frequency, and store the data redundantly in the dynamic area chain to ensure the integrity of the data; (Batch 2) When the external temperature dropped sharply to 15°C, after detecting the violent fluctuation of the ambient temperature, the collection frequency was increased from 20Hz to 25Hz, and the risk index was increased from 0.7 to 0.9, which shows that the method of the present invention can respond quickly. Adjust the collection frequency in response to temperature changes to ensure data collection accuracy in adverse environments and prevent temperature from causing potential damage to the goods. In contrast, traditional methods cannot capture such rapid temperature changes, which can lead to damage to the goods; (Batch 3) When the goods are stagnant for more than 2 hours and environmental conditions deteriorate, increase the collection frequency from 15Hz to 20Hz and increase the risk index from 0.6 to 0.85. At this time, by identifying that the goods may be in an illegal detention state, and using dynamic regional chains to prioritize key data, ensure data security in high-risk areas. Traditional methods cannot respond in time and cannot identify potential risks when faced with such long-term stagnation;

[0155] In addition, combined Figure 2 It can be seen that in 100 batches of cargo transportation, the present invention can adjust the sensor data in real time to ensure that the accuracy of data collection in adverse environments is always maintained at more than 95%; and, reference Figure 3 The method of the present invention obtains accurate positioning of the goods by considering the increase and decrease of the sensor acquisition frequency, thereby ensuring the safety of the goods and preventing the occurrence of goods substitution and theft; finally, Figure 4 , which gives the response speed and response time of various sensors for handling emergencies during the transportation of goods;

[0156] In summary, the simulated emergency events further verified the superiority of the method of the present invention, especially its ability to cope with sudden environmental changes.

[0157] Those skilled in the art will appreciate that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0158] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0161] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0162] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An intelligent anti-theft and tracking method for cargo transportation, characterized in that: include: Obtain the current location, environmental conditions, and transportation status of each batch of goods, adjust the acquisition frequency of sensor signals in the cargo box, and determine the risk area where the current batch of goods is located based on the acquisition frequency of sensor signals; Construct a multi-level dynamic regional chain, pre-process the sensor signal, transmit the pre-processed signal to the dynamic regional chain, and define the rules for uploading cargo data; Construct a cross-regional cargo anomaly prediction model, define multi-regional data fusion rules, identify whether the current batch of cargo data has any anomalies when passing through a certain area, and if so, use the current output result of the cross-regional cargo anomaly prediction model to update the multi-level dynamic regional chain, otherwise, redefine the cargo data chain rules.

2. The intelligent anti-theft and tracking method for cargo transportation according to claim 1, characterized in that: Get the current location, environmental conditions, and transportation status of each batch of goods, including: The latitude and longitude data of the current truck travel are obtained through GPS to obtain the current cargo position signal, and the position signal is smoothed to eliminate the noise around the cargo; According to the sensors deployed in the cargo box, the sensors are normalized to collect environmental data around the cargo in real time; The processed cargo position signal is used to calculate the moving speed and direction of the cargo, and the environmental state vector at the current moment is defined by combining the environmental data with the moving speed and direction of the cargo.

3. The intelligent anti-theft and tracking method for cargo transportation according to claim 2, characterized in that: Adjust the collection frequency of sensor signals in the cargo box, and determine the risk area where the current batch of goods is located based on the collection frequency of sensor signals, including: Introducing a risk weight coefficient, and adjusting the acquisition frequency of the sensor signal in the cargo box according to the environmental state vector; The risk index of the goods is obtained through the adjusted collection frequency and the processed goods location signal, and the risk area where the current batch of goods is located is divided according to the maximum and minimum values ​​of the risk index.

4. The intelligent anti-theft and tracking method for cargo transportation according to claim 1 or 3, characterized in that: Construct a multi-level dynamic regional chain, including: Build an independent regional chain in each cargo transportation area to store the sensor data, cargo location data and environmental data of the cargo in the area; The data information stored in each regional chain is collected and synchronized through the global chain, and the data stored in different regional chains is verified using the consensus algorithm. If the goods cross the regional boundary, that is, from the current regional chain to another regional chain, the other regional chain receives the data on the current regional chain and uses the global chain to exchange data with the current regional chain; If the location of the goods in the regional chain is found to be in a high-risk area during storage, the regional chain will be switched to the dynamic chain, and the dynamic chain will process the data. When the data processing is completed, the dynamic chain will switch back to the regional chain and synchronize the data to the regional chain and the global chain.

5. The intelligent anti-theft and tracking method for cargo transportation according to claim 4, characterized in that: Also includes: The set of sensor data within a certain time window will be packaged into a data block, and the packaged data block will generate a unique identifier through a hash function; For key cargo data whose location is in a high-risk area, it is divided into multiple data slices and stored on multiple regional chain nodes, where each data slice is redundantly stored using erasure codes.

6. The intelligent anti-theft and tracking method for cargo transportation according to claim 4, characterized in that: Preprocess the sensor signal, transmit the preprocessed signal to the dynamic area chain, and define the rules for uploading cargo data, including: Before the cargo data is uploaded to the chain, the standard deviation and mean of the sensor data are calculated, and data points that deviate from the mean by more than the mean of the data block are eliminated; For cargo data in low-risk areas and medium-risk areas, the changes in the cargo data are compressed and stored on the chain.

7. The intelligent anti-theft and tracking method for cargo transportation according to claim 6, characterized in that: Build a cross-region cargo anomaly prediction model, define multi-region data fusion rules, and identify whether the current batch of cargo data is abnormal when passing through a certain area, including: The cargo location data, environmental data and transportation status data are represented by dimension vectors, and the dimension vectors of the cargo location data, environmental data and transportation status data are aggregated by a sliding window; Analyze the sensor data of the goods passing through the current area to obtain the key data types of the area, and assign weights to the areas where the goods have passed in the past and in the future. Fuse the data information stored on the chain of each area with the output results of the cross-regional goods anomaly prediction model, and output the anomaly probability of the goods passing through the area according to the weighted average method; When the abnormal probability exceeds 60%, it indicates that an abnormality exists in the area where the goods pass through.

8. An intelligent anti-theft and tracking system for cargo transportation, based on the intelligent anti-theft and tracking method for cargo transportation according to any one of claims 1 to 7, characterized in that: include: The cargo data and sensor data acquisition module is used to obtain the current location, environmental conditions and transportation status of each batch of cargo, adjust the acquisition frequency of sensor signals in the cargo box, and determine the risk area where the current batch of cargo is located based on the acquisition frequency of sensor signals; The sensor signal processing module is used to build a multi-level dynamic regional chain, pre-process the sensor signal, transmit the pre-processed signal to the dynamic regional chain, and define the rules for uploading cargo data; The cargo data anomaly processing module is used to build a cross-regional cargo anomaly prediction model, define multi-regional data fusion rules, and identify whether the current batch of cargo data has anomalies when passing through a certain area. If there are anomalies, the current output results of the cross-regional cargo anomaly prediction model are used to update the multi-level dynamic regional chain. Otherwise, the cargo data chain rules are redefined.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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